InfoShield: Privacy-Preserving Speech Representations for Mental Health Screening via Information-Theoretic Optimization
Speech-based mental health screening offers scalable depression detection, yet clinical deployment faces a significant barrier: users' privacy concerns about demographic information exposure. Current techniques struggle to resolve this conflict. Adversarial training often fails against unseen threats, whereas Differential Privacy tends to compromise diagnostic performance by injecting noise across all features. This paper presents InfoShield, which minimizes mutual information between speech rep
Record details
Published: 4 June 2026
Source: arXiv
Category: Research
Topics: Privacy · Healthcare
Retrieved: 14 July 2026
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ethics.ai (4 June 2026), “InfoShield: Privacy-Preserving Speech Representations for Mental Health Screening via Information-Theoretic Optimization,” evidence record 1467, https://ethics.ai/record/1467 (originally published by arXiv).
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